A Translational Determination of the Mechanisms of Maladaptive Choice in Opioid Use Disorder

摘要 阿片使用障碍(OUD)的特点是决定以牺牲其他活动为代价使用阿片类药物。 因此，基于实验室的解决这一问题的努力包括阿片类药物选择自我给药程序。 其中包含了非药物替代品来模拟这一定义特征。使用这些程序的研究已经 通常计划竞争的增强剂，以便概率是确定的。然而，这种决定论 结果并不代表真实世界的经验，在这种经验中，与毒品有关的选择的后果 通常是不可预测的。重要的是，在动态、不确定的环境中进行决策会显著改变 选择期权的价值，并需要不断更新期权价值，这需要进行学习过程 以及相关的皮质纹状体网络在OUD中功能异常。动态环境下的决策 已使用概率强化学习选择(PRLC)任务成功建模。整合 在这些具有强化学习(RL)的任务中，计算建模已经被用来捕捉时刻到 动态选择机制的瞬间变化，以及神经科学技术的应用 开始确定潜在的神经生物学。这种方法揭示了以生物学为基础的决策 多发性脑疾病中的异常，但尚未系统地应用于脑损伤的实验研究 OUD，将RL和神经科学的结合方法翻译成OUD是合乎逻辑的，考虑到 阿片类药物使用者典型的不适应选择行为，即不同的强化概率 自然环境，以及已记录在案的学习障碍。因此， 在我们对动态阿片类药物使用决定的机制的理解方面存在严重差距，并且 为应用RL框架填补这些空白提供了强有力的科学前提。该项目提出了严格的PRLC 任务、RL建模、神经记录/fMRI神经成像技术和补充、翻译研究 在老鼠和人类身上的设计。第一组跨物种实验将证明阿片类药物的影响 暴露和戒断对动态决策的影响及揭示神经行为和神经生物学 处理潜在的异常任务表现。第二组实验将使用PRLC任务，在该任务中 静脉注射瑞芬太尼是一种典型的阿片激动剂，具有良好的安全性，可作为替代药物 提供给非药物增强剂，以确定与药物选择相关的行为和神经“概况”，以及 药物选择的增加和减少发生在戒断期间和在大的 大小不同的增强剂。该项目将通过以下方式对外地产生重大影响 建立强化学习理论在不良适应动态研究中的实验应用 OUD中的药物使用决策，以揭示未来可以有针对性的行为和神经机制 开展防治攻坚。

ABSTRACT Opioid use disorder (OUD) is characterized by the decision to use opioids at the expense of other activities. Lab-based efforts to address this problem have therefore included opioid choice self-administration procedures that incorporate a non-drug alternative to model this defining feature. Studies using these procedures have typically scheduled competing reinforcers so that the probabilities are certain. However, such deterministic outcomes are not representative of real-world experiences in which the consequences from drug-related choices are often unpredictable. Importantly, decision-making in a dynamic, uncertain context significantly alters the value of choice options and requires continuous updating of option values, which engages learning processes and related corticostriatal networks that function abnormally in OUD. Decision-making in dynamic environments has been successfully modeled using probabilistic reinforcement-learning choice (PRLC) tasks. The integration of these tasks with reinforcement-learning (RL) computational modeling has been used to capture moment-to- moment changes in the mechanisms of dynamic choice, and the application of neuroscience techniques has begun to identify the underlying neurobiology. This approach has uncovered biologically-based decision-making abnormalities in multiple brain disorders, but has yet to be systematically applied to the experimental study of OUD, The translation of combined RL and neuroscience approaches to OUD is logical considering the maladaptive choice behavior that typifies the disorder, the varying reinforcement probabilities in opioid users’ natural environments, and the learning impairments that have been documented in individuals with OUD. Thus, there are critical gaps in our understanding of the mechanisms underlying dynamic opioid use decisions, and a strong scientific premise for applying an RL framework to fill these gaps. This project proposes rigorous PRLC tasks, RL modeling, neurorecording/fMRI neuroimaging techniques and complementary, translational study designs in rats and humans. The first set of cross-species experiments will demonstrate the impact of opioid exposure and withdrawal on dynamic decision-making and reveal the neurobehavioral and neurobiological processes underlying abnormal task performance. The second set of experiments will use a PRLC task in which intravenous remifentanil, a prototypical opioid agonist with a favorable safety profile, is available as an alternative to a non-drug reinforcer to determine the behavioral and neural “profiles” associated with drug choice, as well as the increases and decreases in drug choice that occur during withdrawal and in the presence of a large magnitude alternative reinforcer, respectively. This project will have a significant impact on the field by establishing the experimental application of reinforcement-learning theory to the study of maladaptive dynamic drug-use decision-making in OUD to reveal behavioral and neural mechanisms that can be targeted for future prevention and treatment development.